KNN on scaled data
The accuracy score on the unscaled wine dataset was decent, but let's see what you can achieve by using standardization. Once again, the knn model as well as the X and y data and labels set have already been created for you.
Cet exercice fait partie du cours
Preprocessing for Machine Learning in Python
Instructions
- Create the
StandardScaler()method, stored in a variable namedscaler. - Scale the training and test features, being careful not to introduce data leakage.
- Fit the
knnmodel to the scaled training data. - Evaluate the model's performance by computing the test set accuracy.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)
# Instantiate a StandardScaler
scaler = ____
# Scale the training and test features
X_train_scaled = ____.____(____)
X_test_scaled = ____.____(____)
# Fit the k-nearest neighbors model to the training data
____.____(____, ____)
# Score the model on the test data
print(____.____(____, ____))